Adaptive Sampling Weights for Machine Learning Convergence
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Solution Overview
Problem
Traditional random sampling methods for training machine learning models may not efficiently utilize large training data sets, particularly when distributions of attribute values are skewed, leading to potential poor generalizability and resource inefficiency.
Innovation Solution
Adaptive sampling techniques are employed, where dynamically modified sampling weights are assigned to training examples based on utility functions and decay parameters, using a tree data structure to efficiently identify and prioritize more valuable or difficult examples, thereby adjusting the probability of their selection in subsequent iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional random sampling methods are used for training machine learning models, then the training process is simple to implement, but the convergence rate is slow and resource efficiency is poor when training data sets are large and have skewed attribute value distributions
Solution Approach 1:
The patent implements dynamic sampling weight adjustment where weights are modified during training iterations based on example performance. The sampling distribution changes over time to focus on more informative examples, transforming the static random sampling into a dynamic adaptive process that improves convergence while managing complexity through structured weight updates
Solution Approach 2:
The patent changes the sampling parameters by introducing and adjusting sampling weights for different training examples. These weights are modified based on utility functions and decay parameters, allowing the system to prioritize certain examples over others, thereby improving convergence rate without requiring complete redesign of the sampling mechanism
2Reliability
If training observations are sampled completely at random from large training data sets, then the sampling process is computationally efficient, but the model generalizability may be poor due to skewed distributions of attribute values
Solution Approach 1:
The patent incorporates feedback mechanisms where the performance of training examples is evaluated and used to update sampling weights. Examples that provide more useful gradient information or are harder to learn from receive higher weights, creating a feedback loop that continuously improves model generalizability by focusing training on informative examples rather than uniformly sampling all data
Solution Approach 2:
The patent performs preliminary analysis of training data characteristics including skewed attribute value distributions before training. This preliminary action allows the system to pre-compute or pre-identify important examples and set initial sampling weights, preparing the training process to handle data skewness efficiently without excessive training time
3Productivity
If adaptive sampling with dynamically modified sampling weights is used, then the convergence rate is enhanced by focusing on informative examples, but the computational complexity and resource usage increase
Solution Approach 1:
The patent applies partial adaptive sampling where not all training examples require full weight update processing in every iteration. The decay parameter causes weights to gradually return toward uniform distribution, and the system selectively updates weights based on example utility, performing adaptive actions only when beneficial rather than universally, thus reducing overall computational resource usage while maintaining convergence benefits
Data Source
AI summary
At a machine learning service, an indication of a training data set for a model is obtained. One or more training iterations of the model are conducted using an adaptive input sampling strategy. In a particular iteration, index values for a set of training observations are selected based on a set of sampling weights, parameters of the model are updated based on results using training observations identified by the index values, and sampling weights are modified. A result obtained from a trained version of the machine learning model is provided.